{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOUvgVp3xnNW"
   },
   "source": [
    "# Target Ensemble\n",
    "\n",
    "Apart from the main target, there are actually many auxilliary targets in the dataset.\n",
    "\n",
    "These targets are fundamentally related to the main target which make them potentially helpful to model. And because these targets have a wide range of correlations to the main targets, it means that we could potentially build some nice ensembles to boost our performance.\n",
    "\n",
    "In this notebook, we will\n",
    "1. Explore the auxilliary targets\n",
    "2. Select our favorite targets to include in the ensemble\n",
    "3. Create an ensemble of models trained on different targets\n",
    "4. Pickle and upload our ensemble model"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!python --version"
   ],
   "metadata": {
    "id": "Ej4poji3G1Df",
    "outputId": "171d8edf-bd43-406a-e782-64ed09624904",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:04.616116Z",
     "start_time": "2025-10-30T20:49:04.188020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.11.11\r\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KD826S8uxnNY",
    "outputId": "2d93c23b-7937-4dba-8dde-1f51df9884c0",
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:15.338187Z",
     "start_time": "2025-10-30T20:49:05.501964Z"
    }
   },
   "source": [
    "# Install dependencies\n",
    "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
    "\n",
    "# Inline plots\n",
    "%matplotlib inline"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.2\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-30T20:49:21.679114Z",
     "start_time": "2025-10-30T20:49:15.358443Z"
    }
   },
   "cell_type": "code",
   "source": "!pip install seaborn",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (0.13.2)\r\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from seaborn) (2.3.4)\r\n",
      "Requirement already satisfied: pandas>=1.2 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from seaborn) (2.3.3)\r\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from seaborn) (3.10.7)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.60.1)\r\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\r\n",
      "Requirement already satisfied: pillow>=8 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\r\n",
      "Requirement already satisfied: pyparsing>=3 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\r\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\r\n",
      "Requirement already satisfied: six>=1.5 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\r\n",
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.2\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VwChLrKexnNa"
   },
   "source": [
    "## 1. Auxilliary Targets\n",
    "\n",
    "Let's start by taking a look at the different targets in the training data."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 511
    },
    "id": "R1I_xkY4xnNa",
    "outputId": "62fdbb5e-df86-4e4e-f648-fe52d726c949",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:23.109387Z",
     "start_time": "2025-10-30T21:25:20.835560Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from numerapi import NumerAPI\n",
    "\n",
    "# Set the data version to one of the most recent versions\n",
    "DATA_VERSION = \"v5.1\"\n",
    "MAIN_TARGET = \"target_cyrusd_20\"\n",
    "TARGET_CANDIDATES = [\n",
    "  MAIN_TARGET,\n",
    "  \"target_victor_20\",\n",
    "  \"target_xerxes_20\",\n",
    "  \"target_teager2b_20\"\n",
    "]\n",
    "FAVORITE_MODEL = \"v51_lgbm_cyrusd20\"\n",
    "\n",
    "# Download data\n",
    "napi = NumerAPI()\n",
    "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
    "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
    "\n",
    "# Load data\n",
    "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
    "feature_cols = feature_metadata[\"feature_sets\"][\"small\"]\n",
    "# use \"medium\" or \"all\" for better performance. Requires more RAM.\n",
    "# features = feature_metadata[\"feature_sets\"][\"medium\"]\n",
    "# features = feature_metadata[\"feature_sets\"][\"all\"]\n",
    "target_cols = feature_metadata[\"targets\"]\n",
    "train = pd.read_parquet(\n",
    "    f\"{DATA_VERSION}/train.parquet\",\n",
    "    columns=[\"era\"] + feature_cols + target_cols\n",
    ")\n",
    "\n",
    "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n",
    "# Comment out the line below to use all the data (higher memory usage, slower model training, potentially better performance)\n",
    "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n",
    "\n",
    "# Print target columns\n",
    "train[[\"era\"] + target_cols]"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:25:21,368 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:25:21,368 INFO numerapi.utils: download complete\n",
      "2025-10-30 14:25:21,785 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:25:21,785 INFO numerapi.utils: download complete\n"
     ]
    },
    {
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       "                   era  target_agnes_20  target_agnes_60  target_alpha_20  \\\n",
       "id                                                                          \n",
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       "\n",
       "                  target_alpha_60  target_bravo_20  target_bravo_60  \\\n",
       "id                                                                    \n",
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       "...                           ...              ...              ...   \n",
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       "nfff87b21e4db902         0.750000         0.500000         0.750000   \n",
       "\n",
       "                  target_caroline_20  target_caroline_60  target_charlie_20  \\\n",
       "id                                                                            \n",
       "n0007b5abb0c3a25            0.250000            0.000000           0.250000   \n",
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       "...                              ...                 ...                ...   \n",
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       "nfff87b21e4db902            0.500000            0.500000           0.500000   \n",
       "\n",
       "                  ...  target_teager2b_60  target_tyler_20  target_tyler_60  \\\n",
       "id                ...                                                         \n",
       "n0007b5abb0c3a25  ...            0.500000         0.250000         0.250000   \n",
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       "nfff87b21e4db902  ...            0.750000         0.750000         0.750000   \n",
       "\n",
       "                  target_victor_20  target_victor_60  target_waldo_20  \\\n",
       "id                                                                      \n",
       "n0007b5abb0c3a25          0.250000          0.250000         0.250000   \n",
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       "...                            ...               ...              ...   \n",
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       "nfff87b21e4db902          0.500000          0.750000         0.500000   \n",
       "\n",
       "                  target_waldo_60  target_xerxes_20  target_xerxes_60   target  \n",
       "id                                                                              \n",
       "n0007b5abb0c3a25         0.000000          0.250000          0.000000 0.250000  \n",
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       "      <th>era</th>\n",
       "      <th>target_agnes_20</th>\n",
       "      <th>target_agnes_60</th>\n",
       "      <th>target_alpha_20</th>\n",
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       "      <th>target_xerxes_20</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0048ac83aff7194</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0055a2401ba6480</th>\n",
       "      <td>0001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffc2d5e4b79a7ae</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffc9844c1c7a6a9</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd79773f4109bb</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff6ab9d6dc0b32</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff87b21e4db902</th>\n",
       "      <td>0573</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688184 rows × 38 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4YzbRO5uxnNa"
   },
   "source": [
    "### The main target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R1o6PJcbxnNa"
   },
   "source": [
    "First thing to note is that `target` is just an alias for the `cyrus` target, so we can drop this column for the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "pP6LnWcExnNa",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:23.136656Z",
     "start_time": "2025-10-30T21:25:23.110278Z"
    }
   },
   "source": [
    "# Drop `target` column\n",
    "assert train[\"target\"].equals(train[MAIN_TARGET])\n",
    "targets_df = train[[\"era\"] + target_cols]"
   ],
   "outputs": [],
   "execution_count": 50
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d46TQDtrxnNb"
   },
   "source": [
    "### Target names\n",
    "\n",
    "At a high level, each target represents a different kind of stock market return\n",
    "- the `name` represents the type of stock market return (eg. residual to market/country/sector vs market/country/style)\n",
    "- the `_20` or `_60` suffix denotes the time horizon of the target (ie. 20 vs 60 market days)\n",
    "\n",
    "The reason why `cyrus` as our main target is because it most closely matches the type of returns we want for our hedge fund. Just like how we are always in search for better features to include in the dataset, we are also always in search for better targets to make our main target. During our research, we often come up with targets we like but not as much as the main target, and these are instead released as auxilliary targets."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "P7uAdarxxnNb",
    "outputId": "ece63dd6-310d-4b40-c863-c22be9170fad",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:23.141524Z",
     "start_time": "2025-10-30T21:25:23.137386Z"
    }
   },
   "source": [
    "# Print target names grouped by name and time horizon\n",
    "pd.set_option('display.max_rows', 100)\n",
    "t20s = [t for t in target_cols if t.endswith(\"_20\")]\n",
    "t60s = [t for t in target_cols if t.endswith(\"_60\")]\n",
    "names = [t.replace(\"target_\", \"\").replace(\"_20\", \"\") for t in t20s]\n",
    "pd.DataFrame({\"name\": names,\"20\": t20s,\"60\": t60s}).set_index(\"name\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          20                  60\n",
       "name                                            \n",
       "agnes        target_agnes_20     target_agnes_60\n",
       "alpha        target_alpha_20     target_alpha_60\n",
       "bravo        target_bravo_20     target_bravo_60\n",
       "caroline  target_caroline_20  target_caroline_60\n",
       "charlie    target_charlie_20   target_charlie_60\n",
       "claudia    target_claudia_20   target_claudia_60\n",
       "cyrusd      target_cyrusd_20    target_cyrusd_60\n",
       "delta        target_delta_20     target_delta_60\n",
       "echo          target_echo_20      target_echo_60\n",
       "jeremy      target_jeremy_20    target_jeremy_60\n",
       "ralph        target_ralph_20     target_ralph_60\n",
       "rowan        target_rowan_20     target_rowan_60\n",
       "sam            target_sam_20       target_sam_60\n",
       "teager2b  target_teager2b_20  target_teager2b_60\n",
       "tyler        target_tyler_20     target_tyler_60\n",
       "victor      target_victor_20    target_victor_60\n",
       "waldo        target_waldo_20     target_waldo_60\n",
       "xerxes      target_xerxes_20    target_xerxes_60"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20</th>\n",
       "      <th>60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>agnes</th>\n",
       "      <td>target_agnes_20</td>\n",
       "      <td>target_agnes_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>target_alpha_20</td>\n",
       "      <td>target_alpha_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bravo</th>\n",
       "      <td>target_bravo_20</td>\n",
       "      <td>target_bravo_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caroline</th>\n",
       "      <td>target_caroline_20</td>\n",
       "      <td>target_caroline_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charlie</th>\n",
       "      <td>target_charlie_20</td>\n",
       "      <td>target_charlie_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>claudia</th>\n",
       "      <td>target_claudia_20</td>\n",
       "      <td>target_claudia_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyrusd</th>\n",
       "      <td>target_cyrusd_20</td>\n",
       "      <td>target_cyrusd_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta</th>\n",
       "      <td>target_delta_20</td>\n",
       "      <td>target_delta_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>echo</th>\n",
       "      <td>target_echo_20</td>\n",
       "      <td>target_echo_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeremy</th>\n",
       "      <td>target_jeremy_20</td>\n",
       "      <td>target_jeremy_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ralph</th>\n",
       "      <td>target_ralph_20</td>\n",
       "      <td>target_ralph_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rowan</th>\n",
       "      <td>target_rowan_20</td>\n",
       "      <td>target_rowan_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sam</th>\n",
       "      <td>target_sam_20</td>\n",
       "      <td>target_sam_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teager2b</th>\n",
       "      <td>target_teager2b_20</td>\n",
       "      <td>target_teager2b_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tyler</th>\n",
       "      <td>target_tyler_20</td>\n",
       "      <td>target_tyler_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>victor</th>\n",
       "      <td>target_victor_20</td>\n",
       "      <td>target_victor_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waldo</th>\n",
       "      <td>target_waldo_20</td>\n",
       "      <td>target_waldo_60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xerxes</th>\n",
       "      <td>target_xerxes_20</td>\n",
       "      <td>target_xerxes_60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 51
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PDkTNQMrxnNb"
   },
   "source": [
    "### Target values\n",
    "\n",
    "Note that some targets are binned into 5 bins while others are binned into 7 bins.\n",
    "\n",
    "Unlike feature values which are integers ranging from 0-4, target values are floats which range from 0-1."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "Uw_4oswnxnNb",
    "outputId": "a208b358-fc87-4423-bf2a-9e8d67007274",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:23.386980Z",
     "start_time": "2025-10-30T21:25:23.142322Z"
    }
   },
   "source": [
    "# Plot target distributions\n",
    "targets_df[TARGET_CANDIDATES].plot(\n",
    "  title=\"Target Distributions\",\n",
    "  kind=\"hist\",\n",
    "  bins=35,\n",
    "  density=True,\n",
    "  figsize=(8, 4),\n",
    "  subplots=True,\n",
    "  layout=(2, 2),\n",
    "  ylabel=\"\",\n",
    "  yticks=[]\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: >, <Axes: >],\n",
       "       [<Axes: >, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 4 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 52
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2DM-mHG_xnNc"
   },
   "source": [
    "It is also important to note that the auxilary targets can be `NaN`, but the primary target will never be `NaN`. Since we are using tree-based models here we won't need to do any special pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "id": "FT3YCXrYxnNc",
    "outputId": "1dbaa0ec-86b7-43ed-dde8-6be3c37c02e6",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:23.540696Z",
     "start_time": "2025-10-30T21:25:23.387893Z"
    }
   },
   "source": [
    "# print number of NaNs per era\n",
    "nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n",
    "nans_per_era[target_cols].plot(figsize=(8, 4), title=\"Number of NaNs per Era\", legend=False)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/1621777384.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Number of NaNs per Era'}, xlabel='era'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 53
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KdPdFvXFxnNc"
   },
   "source": [
    "### Target correlations\n",
    "\n",
    "The targets have a wide range of correlations with each other even though they are all fundamentally related, which should allow the construction of diverse models that ensemble together nicely."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "jAvpw-XvxnNc",
    "outputId": "1c0e3f11-e25a-4df6-a0aa-7c040ddfbd33",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:24.746396Z",
     "start_time": "2025-10-30T21:25:23.541493Z"
    }
   },
   "source": [
    "# Plot correlation matrix of targets\n",
    "import seaborn as sns\n",
    "sns.heatmap(\n",
    "  targets_df[target_cols].corr(),\n",
    "  cmap=\"coolwarm\",\n",
    "  xticklabels=False,\n",
    "  yticklabels=False\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 54
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n4DcPlrixnNd"
   },
   "source": [
    "Since we are ultimately trying to predict the main target, it is perhaps most important to consider each auxilliary target's correlation to it."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "sFN8_azMxnNd",
    "outputId": "4516093b-af13-4ea8-e8c6-efb301a842b6",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:25:24.924255Z",
     "start_time": "2025-10-30T21:25:24.747260Z"
    }
   },
   "source": [
    "(\n",
    "    targets_df[target_cols]\n",
    "    .corrwith(targets_df[MAIN_TARGET])\n",
    "    .sort_values(ascending=False)\n",
    "    .to_frame(\"corr_with_cyrus_v4_20\")\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    corr_with_cyrus_v4_20\n",
       "target                           1.000000\n",
       "target_cyrusd_20                 1.000000\n",
       "target_xerxes_20                 0.942907\n",
       "target_caroline_20               0.922111\n",
       "target_sam_20                    0.911981\n",
       "target_ralph_20                  0.894999\n",
       "target_echo_20                   0.851763\n",
       "target_victor_20                 0.838408\n",
       "target_waldo_20                  0.833260\n",
       "target_delta_20                  0.806752\n",
       "target_bravo_20                  0.801685\n",
       "target_jeremy_20                 0.790352\n",
       "target_charlie_20                0.767458\n",
       "target_alpha_20                  0.765305\n",
       "target_claudia_20                0.745132\n",
       "target_teager2b_20               0.717080\n",
       "target_agnes_20                  0.710171\n",
       "target_tyler_20                  0.707080\n",
       "target_rowan_20                  0.704642\n",
       "target_cyrusd_60                 0.489257\n",
       "target_xerxes_60                 0.485867\n",
       "target_caroline_60               0.482128\n",
       "target_sam_60                    0.479950\n",
       "target_ralph_60                  0.477175\n",
       "target_echo_60                   0.461583\n",
       "target_victor_60                 0.459265\n",
       "target_waldo_60                  0.455948\n",
       "target_delta_60                  0.441798\n",
       "target_bravo_60                  0.441597\n",
       "target_jeremy_60                 0.437988\n",
       "target_charlie_60                0.424056\n",
       "target_alpha_60                  0.423646\n",
       "target_claudia_60                0.410047\n",
       "target_teager2b_60               0.398701\n",
       "target_agnes_60                  0.396759\n",
       "target_tyler_60                  0.396696\n",
       "target_rowan_60                  0.391740"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>corr_with_cyrus_v4_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_cyrusd_20</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_xerxes_20</th>\n",
       "      <td>0.942907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_caroline_20</th>\n",
       "      <td>0.922111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_sam_20</th>\n",
       "      <td>0.911981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ralph_20</th>\n",
       "      <td>0.894999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_echo_20</th>\n",
       "      <td>0.851763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_victor_20</th>\n",
       "      <td>0.838408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_waldo_20</th>\n",
       "      <td>0.833260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_delta_20</th>\n",
       "      <td>0.806752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_bravo_20</th>\n",
       "      <td>0.801685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jeremy_20</th>\n",
       "      <td>0.790352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_charlie_20</th>\n",
       "      <td>0.767458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alpha_20</th>\n",
       "      <td>0.765305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_claudia_20</th>\n",
       "      <td>0.745132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_teager2b_20</th>\n",
       "      <td>0.717080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_agnes_20</th>\n",
       "      <td>0.710171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_tyler_20</th>\n",
       "      <td>0.707080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_rowan_20</th>\n",
       "      <td>0.704642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_cyrusd_60</th>\n",
       "      <td>0.489257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_xerxes_60</th>\n",
       "      <td>0.485867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_caroline_60</th>\n",
       "      <td>0.482128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_sam_60</th>\n",
       "      <td>0.479950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ralph_60</th>\n",
       "      <td>0.477175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_echo_60</th>\n",
       "      <td>0.461583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_victor_60</th>\n",
       "      <td>0.459265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_waldo_60</th>\n",
       "      <td>0.455948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_delta_60</th>\n",
       "      <td>0.441798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_bravo_60</th>\n",
       "      <td>0.441597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jeremy_60</th>\n",
       "      <td>0.437988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_charlie_60</th>\n",
       "      <td>0.424056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alpha_60</th>\n",
       "      <td>0.423646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_claudia_60</th>\n",
       "      <td>0.410047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_teager2b_60</th>\n",
       "      <td>0.398701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_agnes_60</th>\n",
       "      <td>0.396759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_tyler_60</th>\n",
       "      <td>0.396696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_rowan_60</th>\n",
       "      <td>0.391740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 55
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jl7_XIKfxnNd"
   },
   "source": [
    "## 2. Target Selection\n",
    "\n",
    "Our goal is to create an ensemble of models trained on different targets. But which targets should we use?\n",
    "\n",
    "When deciding which model to ensemble, we should consider a few things:\n",
    "\n",
    "- The performance of the predictions of the model trained on the target vs the main target\n",
    "\n",
    "- The correlation between the target and the main target\n",
    "\n",
    "To keep things simple and fast, let's just arbitrarily pick a few 20-day targets to evaluate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4YDRiSoPxnNd"
   },
   "source": [
    "### Model training and generating validation predictions\n",
    "\n",
    "Like usual we train on the training dataset, but this time we do it for each target.\n",
    "\n",
    "Sit back and relax, this will take a while\n",
    "# ☕"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VqBaYwmqxnNd",
    "outputId": "6716cbd7-827c-4593-b65d-25d120de0af5",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:26:32.480930Z",
     "start_time": "2025-10-30T21:25:24.925072Z"
    }
   },
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "models = {}\n",
    "for target in TARGET_CANDIDATES:\n",
    "    model = lgb.LGBMRegressor(\n",
    "        n_estimators=2000,\n",
    "        learning_rate=0.01,\n",
    "        max_depth=5,\n",
    "        num_leaves=2**4-1,\n",
    "        colsample_bytree=0.1\n",
    "    )\n",
    "    # We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
    "    # model = lgb.LGBMRegressor(\n",
    "    #     n_estimators=30_000,\n",
    "    #     learning_rate=0.001,\n",
    "    #     max_depth=10,\n",
    "    #     num_leaves=2**10,\n",
    "    #     colsample_bytree=0.1\n",
    "    #     min_data_in_leaf=10000,\n",
    "    # )\n",
    "    model.fit(\n",
    "        train[feature_cols],\n",
    "        train[target]\n",
    "    )\n",
    "    models[target] = model"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001368 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 210\n",
      "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
      "[LightGBM] [Info] Start training from score 0.500008\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001291 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 210\n",
      "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
      "[LightGBM] [Info] Start training from score 0.500003\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001295 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 210\n",
      "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
      "[LightGBM] [Info] Start training from score 0.500031\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001469 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 210\n",
      "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
      "[LightGBM] [Info] Start training from score 0.499948\n"
     ]
    }
   ],
   "execution_count": 56
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9jD6j1JBxnNe"
   },
   "source": [
    "Then we will generate predictions on the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "id": "Ic9eGKxSxnNe",
    "outputId": "e3522459-1f43-4d45-d20c-a6edfcf6f28a",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:08.081880Z",
     "start_time": "2025-10-30T21:26:32.481705Z"
    }
   },
   "source": [
    "# Download validation data\n",
    "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
    "\n",
    "# Load the validation data, filtering for data_type == \"validation\"\n",
    "validation = pd.read_parquet(\n",
    "    f\"{DATA_VERSION}/validation.parquet\",\n",
    "    columns=[\"era\", \"data_type\"] + feature_cols + target_cols\n",
    ")\n",
    "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
    "del validation[\"data_type\"]\n",
    "\n",
    "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
    "# Comment out the line below to use all the data\n",
    "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
    "\n",
    "# Embargo overlapping eras from training data\n",
    "last_train_era = int(train[\"era\"].unique()[-1])\n",
    "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
    "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
    "\n",
    "# Generate validation predictions for each model\n",
    "for target in TARGET_CANDIDATES:\n",
    "    validation[f\"prediction_{target}\"] = models[target].predict(validation[feature_cols])\n",
    "\n",
    "pred_cols = [f\"prediction_{target}\" for target in TARGET_CANDIDATES]\n",
    "validation[pred_cols]"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:26:32,945 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:26:32,946 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
       "id                                                                           \n",
       "n000c290e4364875                     0.495167                     0.491972   \n",
       "n002a15bc5575bbb                     0.516067                     0.512950   \n",
       "n00309caaa0f955e                     0.513778                     0.512101   \n",
       "n0039cbdcf835708                     0.507834                     0.505156   \n",
       "n004143458984f89                     0.484917                     0.485125   \n",
       "...                                       ...                          ...   \n",
       "nffc45fef92f9990                     0.500651                     0.499039   \n",
       "nffcd993886ef112                     0.486873                     0.487595   \n",
       "nffd2f8483ddb3cc                     0.510465                     0.505686   \n",
       "nffda3e738a622a7                     0.494580                     0.497608   \n",
       "nffdd2cd463ee839                     0.511365                     0.514445   \n",
       "\n",
       "                  prediction_target_xerxes_20  prediction_target_teager2b_20  \n",
       "id                                                                            \n",
       "n000c290e4364875                     0.495561                       0.496844  \n",
       "n002a15bc5575bbb                     0.515098                       0.508923  \n",
       "n00309caaa0f955e                     0.513682                       0.505769  \n",
       "n0039cbdcf835708                     0.506836                       0.504405  \n",
       "n004143458984f89                     0.486912                       0.490126  \n",
       "...                                       ...                            ...  \n",
       "nffc45fef92f9990                     0.499844                       0.502100  \n",
       "nffcd993886ef112                     0.486282                       0.493609  \n",
       "nffd2f8483ddb3cc                     0.509727                       0.506958  \n",
       "nffda3e738a622a7                     0.495073                       0.500247  \n",
       "nffdd2cd463ee839                     0.512808                       0.501620  \n",
       "\n",
       "[942884 rows x 4 columns]"
      ],
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction_target_cyrusd_20</th>\n",
       "      <th>prediction_target_victor_20</th>\n",
       "      <th>prediction_target_xerxes_20</th>\n",
       "      <th>prediction_target_teager2b_20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>n000c290e4364875</th>\n",
       "      <td>0.495167</td>\n",
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       "    <tr>\n",
       "      <th>n002a15bc5575bbb</th>\n",
       "      <td>0.516067</td>\n",
       "      <td>0.512950</td>\n",
       "      <td>0.515098</td>\n",
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       "    <tr>\n",
       "      <th>n00309caaa0f955e</th>\n",
       "      <td>0.513778</td>\n",
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       "      <td>0.505769</td>\n",
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       "    <tr>\n",
       "      <th>n0039cbdcf835708</th>\n",
       "      <td>0.507834</td>\n",
       "      <td>0.505156</td>\n",
       "      <td>0.506836</td>\n",
       "      <td>0.504405</td>\n",
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       "      <th>n004143458984f89</th>\n",
       "      <td>0.484917</td>\n",
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       "      <td>0.486912</td>\n",
       "      <td>0.490126</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>nffc45fef92f9990</th>\n",
       "      <td>0.500651</td>\n",
       "      <td>0.499039</td>\n",
       "      <td>0.499844</td>\n",
       "      <td>0.502100</td>\n",
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       "    <tr>\n",
       "      <th>nffcd993886ef112</th>\n",
       "      <td>0.486873</td>\n",
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       "      <td>0.493609</td>\n",
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       "    <tr>\n",
       "      <th>nffd2f8483ddb3cc</th>\n",
       "      <td>0.510465</td>\n",
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       "    <tr>\n",
       "      <th>nffda3e738a622a7</th>\n",
       "      <td>0.494580</td>\n",
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       "      <td>0.500247</td>\n",
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       "    <tr>\n",
       "      <th>nffdd2cd463ee839</th>\n",
       "      <td>0.511365</td>\n",
       "      <td>0.514445</td>\n",
       "      <td>0.512808</td>\n",
       "      <td>0.501620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942884 rows × 4 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ea2Z98CIxnNe"
   },
   "source": [
    "### Evaluating the performance of each model\n",
    "\n",
    "Now we can evaluate the performance of our models."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "NjuAERHhxnNe",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:08.649806Z",
     "start_time": "2025-10-30T21:27:08.082619Z"
    }
   },
   "source": [
    "# install Numerai's open-source scoring tools\n",
    "!pip install -q --no-deps numerai-tools\n",
    "\n",
    "# import the 2 scoring functions\n",
    "from numerai_tools.scoring import numerai_corr, correlation_contribution"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m25.2\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q8aLpCC3xnNf"
   },
   "source": [
    "As you can see in the performance chart below, models trained on the auxiliary target are able to predict the main target pretty well, but the model trained on the main target performs the best."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "WUvsFi-VxnNf",
    "outputId": "39a65698-cf42-4a58-fe75-eaca8ec2d224",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:10.151678Z",
     "start_time": "2025-10-30T21:27:08.650738Z"
    }
   },
   "source": [
    "prediction_cols = [\n",
    "    f\"prediction_{target}\"\n",
    "    for target in TARGET_CANDIDATES\n",
    "]\n",
    "correlations = validation.groupby(\"era\").apply(\n",
    "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
    ")\n",
    "cumsum_corrs = correlations.cumsum()\n",
    "cumsum_corrs.plot(\n",
    "  title=\"Cumulative Correlation of validation Predictions\",\n",
    "  figsize=(10, 6),\n",
    "  xticks=[]\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/2595143125.py:5: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  correlations = validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 59
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EDUjJ3guxnNf"
   },
   "source": [
    "Looking at the summary metrics below:\n",
    "- the models trained on `victor` and `xerxes` have the highest means, but `victor` is less correlated with `cyrus` than `xerxes` is, which means `victor` could be better in ensembling\n",
    "- the model trained on `teager` has the lowest mean, but `teager` is significantly less correlated with `cyrus` than any other target shown"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 338
    },
    "id": "smz_GLLAxnNf",
    "outputId": "75d86754-9e57-492a-c776-748dd04ca32f",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:10.601008Z",
     "start_time": "2025-10-30T21:27:10.152511Z"
    }
   },
   "source": [
    "def get_summary_metrics(scores, cumsum_scores):\n",
    "    summary_metrics = {}\n",
    "    # per era correlation between predictions of the model trained on this target and cyrus\n",
    "    mean = scores.mean()\n",
    "    std = scores.std()\n",
    "    sharpe = mean / std\n",
    "    rolling_max = cumsum_scores.expanding(min_periods=1).max()\n",
    "    max_drawdown = (rolling_max - cumsum_scores).max()\n",
    "    return {\n",
    "        \"mean\": mean,\n",
    "        \"std\": std,\n",
    "        \"sharpe\": sharpe,\n",
    "        \"max_drawdown\": max_drawdown,\n",
    "    }\n",
    "\n",
    "target_summary_metrics = {}\n",
    "for pred_col in prediction_cols:\n",
    "  target_summary_metrics[pred_col] = get_summary_metrics(\n",
    "      correlations[pred_col], cumsum_corrs[pred_col]\n",
    "  )\n",
    "  # per era correlation between this target and cyrus\n",
    "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
    "      lambda d: d[pred_col].corr(d[MAIN_TARGET])\n",
    "  ).mean()\n",
    "  target_summary_metrics[pred_col].update({\n",
    "      \"mean_corr_with_cryus\": mean_corr_with_cryus\n",
    "  })\n",
    "\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary = pd.DataFrame(target_summary_metrics).T\n",
    "summary"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/3613639555.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/3613639555.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/3613639555.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/3613639555.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                                  mean      std   sharpe  max_drawdown  \\\n",
       "prediction_target_cyrusd_20   0.016520 0.018652 0.885694      0.040911   \n",
       "prediction_target_victor_20   0.015860 0.018508 0.856931      0.039038   \n",
       "prediction_target_xerxes_20   0.016758 0.018566 0.902611      0.043307   \n",
       "prediction_target_teager2b_20 0.013889 0.017037 0.815200      0.052751   \n",
       "\n",
       "                               mean_corr_with_cryus  \n",
       "prediction_target_cyrusd_20                0.016986  \n",
       "prediction_target_victor_20                0.016028  \n",
       "prediction_target_xerxes_20                0.017191  \n",
       "prediction_target_teager2b_20              0.014617  "
      ],
      "text/html": [
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>mean_corr_with_cryus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrusd_20</th>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.018652</td>\n",
       "      <td>0.885694</td>\n",
       "      <td>0.040911</td>\n",
       "      <td>0.016986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_20</th>\n",
       "      <td>0.015860</td>\n",
       "      <td>0.018508</td>\n",
       "      <td>0.856931</td>\n",
       "      <td>0.039038</td>\n",
       "      <td>0.016028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_xerxes_20</th>\n",
       "      <td>0.016758</td>\n",
       "      <td>0.018566</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_teager2b_20</th>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.017037</td>\n",
       "      <td>0.815200</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 60
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yju_5cHJxnNf"
   },
   "source": [
    "### Selecting our favorite target\n",
    "Based on our observations above, it seems like target `victor` is the best candidate target for our ensemble since it has great performance and it is not too correlated with `cyrus`. However, it's interesting to look at how models that are very uncorrelated ensemble together we are going to also look at how `teager` ensembles with `cyrus`.\n",
    "\n",
    "What do you think?\n",
    "\n",
    "Note that this target selection heuristic is extremely basic. In your own research, you will most likely want to consider all targets instead of just our favorites, and may want to experiment with different ways of selecting your ensemble targets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eeqgd58hxnNg"
   },
   "source": [
    "## 3. Ensembling\n",
    "\n",
    "Now that we have reviewed and selected our favorite targets, let's ensemble our predictions and re-evaluate performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rap7tHjGxnNg"
   },
   "source": [
    "### Creating the ensemble\n",
    "\n",
    "For simplicity, we will equal weight the predictions from target `victor` and `cyrus`. Note that this is an extremely basic and arbitrary way of selecting ensemble weights. In your research, you may want to experiment with different ways of setting ensemble weights.\n",
    "\n",
    "Tip: remember to always normalize (percentile rank) your predictions before averaging so that they are comparable!"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "y27jxEUwxnNg",
    "outputId": "17962176-35ec-4d5a-e891-1b51779b961a",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:11.482817Z",
     "start_time": "2025-10-30T21:27:10.601822Z"
    }
   },
   "source": [
    "# Ensemble predictions together with a simple average\n",
    "validation[\"ensemble_cyrus_victor\"] = (\n",
    "    validation\n",
    "    .groupby(\"era\")[[\n",
    "      f\"prediction_{MAIN_TARGET}\",\n",
    "      \"prediction_target_victor_20\",\n",
    "    ]]\n",
    "    .rank(pct=True)\n",
    "    .mean(axis=1)\n",
    ")\n",
    "validation[\"ensemble_cyrus_teager\"] = (\n",
    "    validation\n",
    "    .groupby(\"era\")[[\n",
    "      f\"prediction_{MAIN_TARGET}\",\n",
    "      \"prediction_target_teager2b_20\",\n",
    "    ]]\n",
    "    .rank(pct=True)\n",
    "    .mean(axis=1)\n",
    ")\n",
    "\n",
    "# Print the ensemble predictions\n",
    "prediction_cols = [\n",
    "  \"prediction_target_cyrusd_20\",\n",
    "  \"prediction_target_victor_20\",\n",
    "  \"prediction_target_teager2b_20\",\n",
    "  \"ensemble_cyrus_victor\",\n",
    "  \"ensemble_cyrus_teager\"\n",
    "]\n",
    "validation[prediction_cols]"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
       "id                                                                           \n",
       "n000c290e4364875                     0.495167                     0.491972   \n",
       "n002a15bc5575bbb                     0.516067                     0.512950   \n",
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       "n004143458984f89                     0.484917                     0.485125   \n",
       "...                                       ...                          ...   \n",
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       "nffd2f8483ddb3cc                     0.510465                     0.505686   \n",
       "nffda3e738a622a7                     0.494580                     0.497608   \n",
       "nffdd2cd463ee839                     0.511365                     0.514445   \n",
       "\n",
       "                  prediction_target_teager2b_20  ensemble_cyrus_victor  \\\n",
       "id                                                                       \n",
       "n000c290e4364875                       0.496844               0.183879   \n",
       "n002a15bc5575bbb                       0.508923               0.965491   \n",
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       "...                                         ...                    ...   \n",
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       "nffdd2cd463ee839                       0.501620               0.944981   \n",
       "\n",
       "                  ensemble_cyrus_teager  \n",
       "id                                       \n",
       "n000c290e4364875               0.253067  \n",
       "n002a15bc5575bbb               0.968643  \n",
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       "n004143458984f89               0.020024  \n",
       "...                                 ...  \n",
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       "nffda3e738a622a7               0.368625  \n",
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       "\n",
       "[942884 rows x 5 columns]"
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       "      <th>prediction_target_teager2b_20</th>\n",
       "      <th>ensemble_cyrus_victor</th>\n",
       "      <th>ensemble_cyrus_teager</th>\n",
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       "<p>942884 rows × 5 columns</p>\n",
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      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 61
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sMLgtnXTxnNg"
   },
   "source": [
    "### Evaluating performance of the ensemble\n",
    "Looking at the performance chart below, we can see that the peformance of our ensembles are better than that of the models trained on individual targets. Is this a result you would have expected or does it surprise you?"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "jCRbKxZmxnNg",
    "outputId": "313c0821-f81d-46fe-f54c-e5ce95a583f7",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:13.082998Z",
     "start_time": "2025-10-30T21:27:11.483657Z"
    }
   },
   "source": [
    "correlations = validation.groupby(\"era\").apply(\n",
    "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
    ")\n",
    "cumsum_corrs = correlations.cumsum()\n",
    "\n",
    "cumsum_corrs = pd.DataFrame(cumsum_corrs)\n",
    "cumsum_corrs.plot(\n",
    "    title=\"Cumulative Correlation of validation Predictions\",\n",
    "    figsize=(10, 6),\n",
    "    xticks=[]\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/2734220739.py:1: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  correlations = validation.groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 62
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4FjFZq-hxnNh"
   },
   "source": [
    "Looking at the summary metrics below, we can see that our ensemble seems to have better `mean`, `sharpe`, and `max_drawdown` than our original model. Much more interestingly, however, is that our ensemble with `teager` has even higher sharpe than the ensemble with `victor`!"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "_79JgZULxnNh",
    "outputId": "b843f522-90c8-48a2-af55-a5b11cc19555",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:13.087721Z",
     "start_time": "2025-10-30T21:27:13.083727Z"
    }
   },
   "source": [
    "summary_metrics = get_summary_metrics(correlations, cumsum_corrs)\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary = pd.DataFrame(summary_metrics)\n",
    "summary"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                  mean      std   sharpe  max_drawdown\n",
       "prediction_target_cyrusd_20   0.016520 0.018652 0.885694      0.040911\n",
       "prediction_target_victor_20   0.015860 0.018508 0.856931      0.039038\n",
       "prediction_target_teager2b_20 0.013889 0.017037 0.815200      0.052751\n",
       "ensemble_cyrus_victor         0.016430 0.018735 0.876997      0.040448\n",
       "ensemble_cyrus_teager         0.015782 0.017963 0.878587      0.046404"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrusd_20</th>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.018652</td>\n",
       "      <td>0.885694</td>\n",
       "      <td>0.040911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_20</th>\n",
       "      <td>0.015860</td>\n",
       "      <td>0.018508</td>\n",
       "      <td>0.856931</td>\n",
       "      <td>0.039038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_teager2b_20</th>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.017037</td>\n",
       "      <td>0.815200</td>\n",
       "      <td>0.052751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_victor</th>\n",
       "      <td>0.016430</td>\n",
       "      <td>0.018735</td>\n",
       "      <td>0.876997</td>\n",
       "      <td>0.040448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_teager</th>\n",
       "      <td>0.015782</td>\n",
       "      <td>0.017963</td>\n",
       "      <td>0.878587</td>\n",
       "      <td>0.046404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 63
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kns8BQ1kL2mE"
   },
   "source": [
    "You can see below that ensembling also improves MMC performance significantly."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "id": "k9jKnsnuL1se",
    "outputId": "85142579-bb14-4847-e6ba-108aa6caf235",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:14.645288Z",
     "start_time": "2025-10-30T21:27:13.088355Z"
    }
   },
   "source": [
    "from numerai_tools.scoring import correlation_contribution\n",
    "\n",
    "# Download and join in the meta_model for the validation eras\n",
    "napi.download_dataset(f\"v4.3/meta_model.parquet\", round_num=842)\n",
    "validation[\"meta_model\"] = pd.read_parquet(\n",
    "    f\"v4.3/meta_model.parquet\"\n",
    ")[\"numerai_meta_model\"]\n",
    "\n",
    "def get_mmc(validation, meta_model_col):\n",
    "    # Compute the per-era mmc between our predictions, the meta model, and the target values\n",
    "    per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
    "        lambda x: correlation_contribution(\n",
    "            x[prediction_cols], x[meta_model_col], x[\"target\"]\n",
    "        )\n",
    "    )\n",
    "\n",
    "    cumsum_mmc = per_era_mmc.cumsum()\n",
    "\n",
    "    # compute summary metrics\n",
    "    summary_metrics = get_summary_metrics(per_era_mmc, cumsum_mmc)\n",
    "    summary = pd.DataFrame(summary_metrics)\n",
    "\n",
    "    return per_era_mmc, cumsum_mmc, summary\n",
    "\n",
    "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"meta_model\")\n",
    "# plot the cumsum mmc performance\n",
    "cumsum_mmc.plot(\n",
    "  title=\"Cumulative MMC of Neutralized Predictions\",\n",
    "  figsize=(10, 6),\n",
    "  xticks=[]\n",
    ")\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:27:13,547 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:27:13,547 INFO numerapi.utils: download complete\n",
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/4064323070.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                                  mean      std   sharpe  max_drawdown\n",
       "prediction_target_cyrusd_20   0.006842 0.016903 0.404755      0.063878\n",
       "prediction_target_victor_20   0.006539 0.017877 0.365776      0.076714\n",
       "prediction_target_teager2b_20 0.007756 0.016656 0.465640      0.082137\n",
       "ensemble_cyrus_victor         0.006798 0.017585 0.386574      0.072389\n",
       "ensemble_cyrus_teager         0.007615 0.016783 0.453734      0.074955"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrusd_20</th>\n",
       "      <td>0.006842</td>\n",
       "      <td>0.016903</td>\n",
       "      <td>0.404755</td>\n",
       "      <td>0.063878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_20</th>\n",
       "      <td>0.006539</td>\n",
       "      <td>0.017877</td>\n",
       "      <td>0.365776</td>\n",
       "      <td>0.076714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_teager2b_20</th>\n",
       "      <td>0.007756</td>\n",
       "      <td>0.016656</td>\n",
       "      <td>0.465640</td>\n",
       "      <td>0.082137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_victor</th>\n",
       "      <td>0.006798</td>\n",
       "      <td>0.017585</td>\n",
       "      <td>0.386574</td>\n",
       "      <td>0.072389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_teager</th>\n",
       "      <td>0.007615</td>\n",
       "      <td>0.016783</td>\n",
       "      <td>0.453734</td>\n",
       "      <td>0.074955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 64
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bijsOnqqOqpf"
   },
   "source": [
    "#### Benchmark Models\n",
    "\n",
    "It's no accident that a model trained on `teager` nicely ensembles with `cyrus`. We have seen in our research that models trained or ensembled using `teager` perform well. We even released a benchmark for a [teager ensemble](https://numer.ai/v42_teager_ensemble). We submit predictions for all internally known models [here](https://numer.ai/~benchmark_models) and release files with their predictions."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "id": "QNSLiLDWOx6D",
    "outputId": "18224a74-f6de-4bc8-cab1-84d7552b1873",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:15.773073Z",
     "start_time": "2025-10-30T21:27:14.646448Z"
    }
   },
   "source": [
    "# download Numerai's benchmark models\n",
    "napi.download_dataset(f\"{DATA_VERSION}/validation_benchmark_models.parquet\")\n",
    "benchmark_models = pd.read_parquet(\n",
    "    f\"{DATA_VERSION}/validation_benchmark_models.parquet\"\n",
    ")\n",
    "benchmark_models"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:27:15,147 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:27:15,149 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                   era  v51_lgbm_cyrusd20  v51_lgbm_teager2b20  \\\n",
       "id                                                               \n",
       "n000101811a8a843  0575           0.558476             0.595672   \n",
       "n001e1318d5072ac  0575           0.706009             0.651645   \n",
       "n002a9c5ab785cbb  0575           0.690093             0.890558   \n",
       "n002ccf6d0e8c5ad  0575           0.932225             0.947961   \n",
       "n0041544c345c91d  0575           0.769134             0.764306   \n",
       "...                ...                ...                  ...   \n",
       "nffd9cf2c992c881  1191           0.417104             0.505254   \n",
       "nfff0fbd1837e76f  1191           0.822534             0.869819   \n",
       "nfff3b8261e8af0a  1191           0.014594             0.005838   \n",
       "nfff3cb2970e386b  1191           0.958552             0.940163   \n",
       "nfff8ba3632ff9b9  1191           0.580998             0.215412   \n",
       "\n",
       "                  v51_lgbm_cyrusd60  v51_lgbm_teager2b60  \n",
       "id                                                        \n",
       "n000101811a8a843           0.818670             0.701717  \n",
       "n001e1318d5072ac           0.532546             0.581009  \n",
       "n002a9c5ab785cbb           0.686695             0.725322  \n",
       "n002ccf6d0e8c5ad           0.925787             0.905937  \n",
       "n0041544c345c91d           0.599964             0.837625  \n",
       "...                             ...                  ...  \n",
       "nffd9cf2c992c881           0.595301             0.537215  \n",
       "nfff0fbd1837e76f           0.907180             0.955633  \n",
       "nfff3b8261e8af0a           0.038237             0.024518  \n",
       "nfff3cb2970e386b           0.968914             0.969644  \n",
       "nfff8ba3632ff9b9           0.608727             0.260800  \n",
       "\n",
       "[3827884 rows x 5 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>era</th>\n",
       "      <th>v51_lgbm_cyrusd20</th>\n",
       "      <th>v51_lgbm_teager2b20</th>\n",
       "      <th>v51_lgbm_cyrusd60</th>\n",
       "      <th>v51_lgbm_teager2b60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n000101811a8a843</th>\n",
       "      <td>0575</td>\n",
       "      <td>0.558476</td>\n",
       "      <td>0.595672</td>\n",
       "      <td>0.818670</td>\n",
       "      <td>0.701717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n001e1318d5072ac</th>\n",
       "      <td>0575</td>\n",
       "      <td>0.706009</td>\n",
       "      <td>0.651645</td>\n",
       "      <td>0.532546</td>\n",
       "      <td>0.581009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n002a9c5ab785cbb</th>\n",
       "      <td>0575</td>\n",
       "      <td>0.690093</td>\n",
       "      <td>0.890558</td>\n",
       "      <td>0.686695</td>\n",
       "      <td>0.725322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n002ccf6d0e8c5ad</th>\n",
       "      <td>0575</td>\n",
       "      <td>0.932225</td>\n",
       "      <td>0.947961</td>\n",
       "      <td>0.925787</td>\n",
       "      <td>0.905937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0041544c345c91d</th>\n",
       "      <td>0575</td>\n",
       "      <td>0.769134</td>\n",
       "      <td>0.764306</td>\n",
       "      <td>0.599964</td>\n",
       "      <td>0.837625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffd9cf2c992c881</th>\n",
       "      <td>1191</td>\n",
       "      <td>0.417104</td>\n",
       "      <td>0.505254</td>\n",
       "      <td>0.595301</td>\n",
       "      <td>0.537215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff0fbd1837e76f</th>\n",
       "      <td>1191</td>\n",
       "      <td>0.822534</td>\n",
       "      <td>0.869819</td>\n",
       "      <td>0.907180</td>\n",
       "      <td>0.955633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff3b8261e8af0a</th>\n",
       "      <td>1191</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>0.005838</td>\n",
       "      <td>0.038237</td>\n",
       "      <td>0.024518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff3cb2970e386b</th>\n",
       "      <td>1191</td>\n",
       "      <td>0.958552</td>\n",
       "      <td>0.940163</td>\n",
       "      <td>0.968914</td>\n",
       "      <td>0.969644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfff8ba3632ff9b9</th>\n",
       "      <td>1191</td>\n",
       "      <td>0.580998</td>\n",
       "      <td>0.215412</td>\n",
       "      <td>0.608727</td>\n",
       "      <td>0.260800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3827884 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 65
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dZNr_xbQWJsf"
   },
   "source": [
    "Because models trained on newer targets perform so well and we release their predictions, it's likely many users will begin to shift their models to include newer data and targets. By extension, the Meta Model will begin to include information from from these new targets.\n",
    "\n",
    "This means that MMC over the validation period may not be truly indicative of out-of-sample performance. The Meta Model over the early validation period did not have access to newer data/targets and MMC over the validation period may be misleading.\n",
    "\n",
    "So if the Meta Model was much closer to our teager ensemble, what would your MMC look like?"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 785
    },
    "id": "OcUNnnkUWnwg",
    "outputId": "65de24b0-9515-4205-ede5-8d5649fadc23",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:17.435239Z",
     "start_time": "2025-10-30T21:27:15.773775Z"
    }
   },
   "source": [
    "validation[FAVORITE_MODEL] = benchmark_models[FAVORITE_MODEL]\n",
    "\n",
    "\n",
    "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, FAVORITE_MODEL)\n",
    "# plot the cumsum mmc performance\n",
    "cumsum_mmc.plot(\n",
    "  title=\"Contribution of Neutralized Predictions to Numerai's Teager Ensemble\",\n",
    "  figsize=(10, 6),\n",
    "  xticks=[]\n",
    ")\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/4064323070.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                                  mean      std   sharpe  max_drawdown\n",
       "prediction_target_cyrusd_20   0.002652 0.017295 0.153316      0.141010\n",
       "prediction_target_victor_20   0.001389 0.018016 0.077093      0.186534\n",
       "prediction_target_teager2b_20 0.002524 0.016196 0.155837      0.143033\n",
       "ensemble_cyrus_victor         0.002026 0.017831 0.113601      0.167827\n",
       "ensemble_cyrus_teager         0.002712 0.016724 0.162158      0.139735"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrusd_20</th>\n",
       "      <td>0.002652</td>\n",
       "      <td>0.017295</td>\n",
       "      <td>0.153316</td>\n",
       "      <td>0.141010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_20</th>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.018016</td>\n",
       "      <td>0.077093</td>\n",
       "      <td>0.186534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_teager2b_20</th>\n",
       "      <td>0.002524</td>\n",
       "      <td>0.016196</td>\n",
       "      <td>0.155837</td>\n",
       "      <td>0.143033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_victor</th>\n",
       "      <td>0.002026</td>\n",
       "      <td>0.017831</td>\n",
       "      <td>0.113601</td>\n",
       "      <td>0.167827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_teager</th>\n",
       "      <td>0.002712</td>\n",
       "      <td>0.016724</td>\n",
       "      <td>0.162158</td>\n",
       "      <td>0.139735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 66
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1KSqVvJBxnNh"
   },
   "source": [
    "Ouch. Our teager models actually perform the worst. This means we aren't adding very useful signal to a model that Numerai already created, but this should not be surprising since we are training basically the same model. The model trained with `xerxes`, however, still does well against Numerai's model. What do you think this means?\n",
    "\n",
    "It's also helpful to if we measured the contribution of your models to all of Numerai's benchmark models. We call this Benchmark Model Contribution or `BMC`.  On the website, `BMC` measures your model's contribution to a weighted ensemble of all of our Benchmark Models.\n",
    "\n",
    "This is an important metric to track because it tells you how additive your model is to Numerai's known models and, by extension, how additive you might be to the Meta Model in the future.\n",
    "\n",
    "To keep things simple, we will use an unweighted ensemble of Numerai's Benchmarks to measure your models' BMC, let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 785
    },
    "id": "39UfnEmifTMh",
    "outputId": "827dd9fb-4682-418c-eecd-f3d1e5c90114",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:23.504155Z",
     "start_time": "2025-10-30T21:27:17.436152Z"
    }
   },
   "source": [
    "validation[\"numerai_benchmark\"] = (\n",
    "    benchmark_models\n",
    "    .groupby(\"era\")\n",
    "    .apply(lambda x: x.mean(axis=1))\n",
    "    .reset_index()\n",
    "    .set_index(\"id\")[0]\n",
    ")\n",
    "\n",
    "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"numerai_benchmark\")\n",
    "# plot the cumsum mmc performance\n",
    "cumsum_mmc.plot(\n",
    "  title=\"Cumulative BMC of Neutralized Predictions\",\n",
    "  figsize=(10, 6),\n",
    "  xticks=[]\n",
    ")\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
    "summary"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_80671/4064323070.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                                  mean      std   sharpe  max_drawdown\n",
       "prediction_target_cyrusd_20   0.002972 0.017989 0.165215      0.141417\n",
       "prediction_target_victor_20   0.001181 0.018387 0.064245      0.196101\n",
       "prediction_target_teager2b_20 0.001602 0.016220 0.098741      0.164488\n",
       "ensemble_cyrus_victor         0.002075 0.018366 0.112979      0.172002\n",
       "ensemble_cyrus_teager         0.002397 0.017104 0.140147      0.150087"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prediction_target_cyrusd_20</th>\n",
       "      <td>0.002972</td>\n",
       "      <td>0.017989</td>\n",
       "      <td>0.165215</td>\n",
       "      <td>0.141417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_victor_20</th>\n",
       "      <td>0.001181</td>\n",
       "      <td>0.018387</td>\n",
       "      <td>0.064245</td>\n",
       "      <td>0.196101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction_target_teager2b_20</th>\n",
       "      <td>0.001602</td>\n",
       "      <td>0.016220</td>\n",
       "      <td>0.098741</td>\n",
       "      <td>0.164488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_victor</th>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.018366</td>\n",
       "      <td>0.112979</td>\n",
       "      <td>0.172002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_cyrus_teager</th>\n",
       "      <td>0.002397</td>\n",
       "      <td>0.017104</td>\n",
       "      <td>0.140147</td>\n",
       "      <td>0.150087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 67
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q-FSdGrhqWkD"
   },
   "source": [
    "Looking at the results above, none of these models seem very additive to models that Numerai can already create. It will take some research and experimentation to find something additive to Numerai's benchmarks.\n",
    "\n",
    "Ensembling models trained on different targets can be a very fruitful avenue of research. However, it is completely up to you whether or not to create an ensemble - there are many great performing models that don't make use of the auxilliary targets at all.\n",
    "\n",
    "If you are interested in learning more about targets, we highly encourage you to read up on these forum posts\n",
    "- https://forum.numer.ai/t/how-to-ensemble-models/4034\n",
    "- https://forum.numer.ai/t/target-jerome-is-dominating-and-thats-weird/6513"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ihVoBcnhxnNq"
   },
   "source": [
    "## 4. Model Upload\n",
    "To wrap up this notebook, let's pickle and upload our ensemble.\n",
    "\n",
    "As usual, we will be wrapping our submission pipeline into a function. Since we already have our favorite targets and trained models in memory, we can simply reference them in our function.  "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "WWdOTGy4xnNq",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:23.507140Z",
     "start_time": "2025-10-30T21:27:23.504992Z"
    }
   },
   "source": [
    "# we now give you access to the live_benchmark_models if you want to use them in your ensemble\n",
    "def predict_ensemble(\n",
    "    live_features: pd.DataFrame,\n",
    "    live_benchmark_models: pd.DataFrame\n",
    ") -> pd.DataFrame:\n",
    "    favorite_targets = [\n",
    "        'target_cyrusd_20',\n",
    "        'target_teager2b_20'\n",
    "    ]\n",
    "    # generate predictions from each model\n",
    "    predictions = pd.DataFrame(index=live_features.index)\n",
    "    for target in favorite_targets:\n",
    "        predictions[target] = models[target].predict(live_features[feature_cols])\n",
    "    # ensemble predictions\n",
    "    ensemble = predictions.rank(pct=True).mean(axis=1)\n",
    "    # format submission\n",
    "    submission = ensemble.rank(pct=True, method=\"first\")\n",
    "    return submission.to_frame(\"prediction\")"
   ],
   "outputs": [],
   "execution_count": 68
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "id": "kPq_ATf0xnNr",
    "outputId": "53bef369-0a53-4c1e-fd62-d9d4d6ec2c53",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:24.181411Z",
     "start_time": "2025-10-30T21:27:23.508067Z"
    }
   },
   "source": [
    "# Quick test\n",
    "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
    "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=feature_cols)\n",
    "predict_ensemble(live_features, benchmark_models)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-30 14:27:23,952 INFO numerapi.utils: target file already exists\n",
      "2025-10-30 14:27:23,953 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                  prediction\n",
       "id                          \n",
       "n000216cc4494713    0.786694\n",
       "n000a84b4f40eca0    0.929676\n",
       "n0011889f1d1f315    0.019259\n",
       "n0020dbf0bd45ef4    0.045083\n",
       "n002f65628075a89    0.154362\n",
       "...                      ...\n",
       "nffcea5e1ee074fc    0.805953\n",
       "nffeb4bd146a7d3f    0.332361\n",
       "nfffa1a1e897fb6f    0.382988\n",
       "nfffecb08bf96924    0.801722\n",
       "nffffb5d0acb74c0    0.978115\n",
       "\n",
       "[6854 rows x 1 columns]"
      ],
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n000216cc4494713</th>\n",
       "      <td>0.786694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n000a84b4f40eca0</th>\n",
       "      <td>0.929676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0011889f1d1f315</th>\n",
       "      <td>0.019259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0020dbf0bd45ef4</th>\n",
       "      <td>0.045083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n002f65628075a89</th>\n",
       "      <td>0.154362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffcea5e1ee074fc</th>\n",
       "      <td>0.805953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffeb4bd146a7d3f</th>\n",
       "      <td>0.332361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfffa1a1e897fb6f</th>\n",
       "      <td>0.382988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfffecb08bf96924</th>\n",
       "      <td>0.801722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nffffb5d0acb74c0</th>\n",
       "      <td>0.978115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6854 rows × 1 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 69
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "5VTrZ1Q6xnNr",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:24.288711Z",
     "start_time": "2025-10-30T21:27:24.182627Z"
    }
   },
   "source": [
    "# Use the cloudpickle library to serialize your function and its dependencies\n",
    "import cloudpickle\n",
    "p = cloudpickle.dumps(predict_ensemble)\n",
    "with open(\"target_ensemble.pkl\", \"wb\") as f:\n",
    "    f.write(p)"
   ],
   "outputs": [],
   "execution_count": 70
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "RiJjBD-zxnNr",
    "outputId": "dd372d69-e8f7-4fec-8211-2078a3e18c6d",
    "ExecuteTime": {
     "end_time": "2025-10-30T21:27:24.292419Z",
     "start_time": "2025-10-30T21:27:24.289673Z"
    }
   },
   "source": [
    "# Download file if running in Google Colab\n",
    "try:\n",
    "    from google.colab import files\n",
    "    files.download('target_ensemble.pkl')\n",
    "except:\n",
    "    pass"
   ],
   "outputs": [],
   "execution_count": 71
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jxO2YzmIxnNr"
   },
   "source": [
    "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
 ],
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